CN104977581A - Multi-moving target situation awareness method, device and system - Google Patents
Multi-moving target situation awareness method, device and system Download PDFInfo
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- CN104977581A CN104977581A CN201510416468.3A CN201510416468A CN104977581A CN 104977581 A CN104977581 A CN 104977581A CN 201510416468 A CN201510416468 A CN 201510416468A CN 104977581 A CN104977581 A CN 104977581A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
- G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
- G01S13/726—Multiple target tracking
Abstract
The invention discloses a multi-moving target situation awareness method, a device and a system. The multi-moving target situation awareness method comprises steps: a target measurement collection is acquired; the target measurement collection is sent to at least two parallel sequential Monte Carlo-probability hypothesis density filters for processing, and a processing result is outputted; and the processing result is comprehensively analyzed, and a moving situation of each target in the target measurement collection is acquired. The parallel sequential Monte Carlo-probability hypothesis density filters are adopted for analyzing and processing multiple moving targets, true paths of the multiple targets are accurately and clearly reflected by multi-moving target situation awareness, and the number of targets at each moment can be accurately estimated.
Description
Technical field
The present invention relates to target following technical field, particularly a kind of multiple mobile object Situation Awareness method, Apparatus and system.
Background technology
Situation Awareness refers under specific space-time, to the discovering of element each in dynamic environment or object, understands and prediction to to-be.For multiple mobile object scene, under interference, clutter environment, the target numbers become when estimating based on the measurement information comprising noise and flight path, be the vital task of multiple mobile object Situation Awareness, also play profound influence to the validity of follow-up decision.
The number of multiple mobile object is estimated to regard with Track In Track problem the monotrack problem be separated as by traditional multi-object tracking method, and adopts suitable wave filter to be followed the tracks of to each target.The core of this method is measurement-track association, and the process of each targetpath is distributed in the observed quantity be about under interference, clutter environment.But in interference, clutter is intensive, or when measurement noise is larger, this assigning process is easily made mistakes.In addition, when target numbers is more, the calculated amount of association process is very large, is unfavorable for real-time application.
Based on stochastic finite collection and point process theory, recently probability hypothesis density (the Probabilityhypothesis density proposed, being called for short PHD) wave filter is a kind of multiple goal density estimator, provides a kind of new visual angle for solving multiple target tracking problem.The wave filter of PHD, without the need to measuring and track association, also measuring and interference, clutter without the need to distinguishing target, thus enormously simplify processing procedure.But traditional PHD wave filter still includes many integration items being difficult to explicit derivation.For such problem, propose sequential Monte Carlo (Sequential Monte Carlo the is called for short SMC) method of PHD wave filter, solve the Project Realization problem of PHD.But due to the random sampling of SMC method, the estimation for dbjective state has certain randomness, may affect the judgement to multiple goal situation.
Summary of the invention
In view of the above problems, propose the present invention to overcome the problems referred to above or to solve the problem at least in part, technical scheme of the present invention is achieved in that
On the one hand, the invention provides a kind of multiple mobile object Situation Awareness method, comprising:
Obtain the measurement set of target;
The measurement set of described target is sent at least two parallel sequential Monte Carlo-probability hypothesis density wave filters to process, and output processing result;
Described result is comprehensively analyzed, obtains the state of motion of measurement set each target interior of described target.
Preferably, the type that type of variables depends on Situation Awareness System sensor is measured in the measurement set of described target.
Preferably, described Situation Awareness System sensor comprises: active radar system and passive reconnaissance system; The measurement variable of described active radar system comprises: distance, azimuth pitch angle; The measurement variable of described passive reconnaissance system comprises: azimuth pitch angle, angular velocity, the time difference, frequency difference.
Preferably, described sequential Monte Carlo-probability hypothesis density wave filter, specifically comprises: initialization, and probability hypothesis density is predicted, probability hypothesis density upgrades, and number of targets is estimated, resampling, and dbjective state is extracted.
Preferably, the method also comprises:
Store the result of sequential Monte Carlo described in each-probability hypothesis density wave filter;
Show the state of motion of measurement set each target interior of described target.
On the other hand, the invention provides a kind of multiple mobile object Situation Awareness device, comprising:
Target measures acquisition module, for obtaining the measurement set of target;
Sequential Monte Carlo-probability hypothesis density filter parallel processing module, processes for the measurement set of described target being sent at least two parallel sequential Monte Carlo-probability hypothesis density wave filters, and output processing result;
Results analyses module, for described result comprehensively being analyzed, obtains the state of motion of measurement set each target interior of described target.
Preferably, the type that type of variables depends on Situation Awareness System sensor is measured in the measurement set of described target.
Preferably, described Situation Awareness System sensor comprises: active radar system and passive reconnaissance system; The measurement variable of described active radar system comprises: distance, azimuth pitch angle; The measurement variable of described passive reconnaissance system comprises: azimuth pitch angle, angular velocity, the time difference, frequency difference.
Preferably, described sequential Monte Carlo-probability hypothesis density wave filter, specifically comprises: initialization, and probability hypothesis density is predicted, probability hypothesis density upgrades, and number of targets is estimated, resampling, and dbjective state is extracted.
Preferably, the method also comprises:
Store the result of sequential Monte Carlo described in each-probability hypothesis density wave filter;
Show the state of motion of measurement set each target interior of described target.
Again on the one hand, the invention provides a kind of multiple mobile object Situation Awareness System, comprising: an as above arbitrary described multiple mobile object Situation Awareness device.
Technical scheme of the present invention carries out analyzing and processing by adopting parallel sequential Monte Carlo and probability hypothesis density wave filter to multiple mobile object, thus make multiple mobile object Situation Awareness reflect multiobject true flight path more accurately, clearly, estimate the number of targets in each moment more exactly.
Accompanying drawing explanation
A kind of multiple mobile object Situation Awareness method flow diagram that Fig. 1 provides for the embodiment of the present invention;
A kind of multiple mobile object Situation Awareness apparatus structure schematic diagram that Fig. 2 provides for the embodiment of the present invention;
Fig. 3 is multiple goal actual flight path schematic diagram in multiple mobile object Situation Awareness process;
Fig. 4 is non-filtered in multiple mobile object Situation Awareness process, directly according to measuring the targetpath schematic diagram estimated;
Fig. 5 is flight path estimated result schematic diagram in multiple mobile object Situation Awareness process;
Fig. 6 is the track estimation result schematic diagram based on single SMC-PHD wave filter;
Number of targets estimated result schematic diagram in a kind of multiple mobile object Situation Awareness process that Fig. 7 provides for the embodiment of the present invention;
A kind of multiple mobile object Situation Awareness System structural representation that Fig. 8 provides for the embodiment of the present invention.
Embodiment
For making the object, technical solutions and advantages of the present invention clearly, below in conjunction with accompanying drawing, embodiment of the present invention is described further in detail.
As a kind of multiple mobile object Situation Awareness method flow diagram that Fig. 1 provides for being depicted as the embodiment of the present invention; The method comprises:
101: the measurement set obtaining target;
102: the measurement set of described target is sent at least two parallel sequential Monte Carlo-probability hypothesis density wave filters and processes, and output processing result;
103: described result is comprehensively analyzed, obtain the state of motion of measurement set each target interior of described target.
It should be noted that, in the measurement set of described target, measure the type that type of variables depends on Situation Awareness System sensor.
Also it should be noted that, described Situation Awareness System sensor comprises: active radar system and passive reconnaissance system; The measurement variable of described active radar system comprises: distance, azimuth pitch angle; The measurement variable of described passive reconnaissance system comprises: azimuth pitch angle, angular velocity, the time difference, frequency difference.
Also it should be noted that, described sequential Monte Carlo-probability hypothesis density wave filter, specifically comprises: initialization, and probability hypothesis density is predicted, probability hypothesis density upgrades, and number of targets is estimated, resampling, and dbjective state is extracted.
Also it should be noted that, the method also comprises:
Store the result of sequential Monte Carlo described in each-probability hypothesis density wave filter;
Show the state of motion of measurement set each target interior of described target.
Based on above embodiment, as shown in Figure 2, be a kind of multiple mobile object Situation Awareness apparatus structure schematic diagram that the embodiment of the present invention provides; This multiple mobile object Situation Awareness device comprises:
Target measures acquisition module 201, for obtaining the measurement set of target;
Sequential Monte Carlo-probability hypothesis density filter parallel processing module 202, processes for the measurement set of described target being sent at least two parallel sequential Monte Carlo-probability hypothesis density wave filters, and output processing result;
Results analyses module 203, for described result comprehensively being analyzed, obtains the state of motion of measurement set each target interior of described target.
It should be noted that, in the measurement set of described target, measure the type that type of variables depends on Situation Awareness System sensor; Described Situation Awareness System sensor comprises: active radar system and passive reconnaissance system; The measurement variable of described active radar system comprises: distance, azimuth pitch angle; The measurement variable of described passive reconnaissance system comprises: azimuth pitch angle, angular velocity, the time difference, frequency difference.
Also it should be noted that, described sequential Monte Carlo-probability hypothesis density wave filter, specifically comprises: initialization, and probability hypothesis density is predicted, probability hypothesis density upgrades, and number of targets is estimated, resampling, and dbjective state is extracted.
Also it should be noted that, this device also comprises:
Memory module, for storing the result of sequential Monte Carlo described in each-probability hypothesis density wave filter;
Display module, for show described target measurement set in the state of motion of each target.
Based on above multiple mobile object Situation Awareness method and device, principle of work of the present invention is described in detail;
As shown in Figure 2, multiple mobile object Situation Awareness device of the present invention comprises: target measures acquisition module, sequential Monte Carlo-probability hypothesis density filter parallel processing module (being called for short SMC-PHD filter parallel processing module), results analyses module.
Described target measures acquisition module for gathering the measurement set Z of target
k={ z
k, 1..., z
k,M∈ F (Z), wherein Z
kfor target measures collection, M is for measuring number, z
k, 1..., z
k,Mfor various measurement variable, F (Z) is for measuring space.Measure the type that type of variables depends on Situation Awareness System sensor.For active radar system, common measurement variable has distance, azimuth pitch angle; For passive reconnaissance system, common measurement variable has azimuth pitch angle, angular velocity, the time difference, frequency difference etc.
Described SMC-PHD filter parallel processing module, includes multiple SMC-PHD wave filter, and the output of each SMC-PHD wave filter is the Target state estimator collection in each moment
with number of targets estimated value
wherein
for SMC-PHD wave filter j is at the Target state estimator collection of moment k,
for SMC-PHD wave filter j is in the number of targets estimated value of moment k.
generally include the location estimation value of target, velocity estimation value, sometimes also comprise acceleration estimation value etc.
Each SMC-PHD wave filter comprises initialization, PHD prediction, and PHD upgrades, and number of targets is estimated, resampling, dbjective state extracts this 6 steps.Concrete derivation about SMC-PHD wave filter is as follows:
Initialization: suppose that estimating target number is N
0, each destination sample H particle, then primary number is L
0=H × N
0.Given multiple goal prior probability is p
0(X
0), to original state random set X
0sampling, obtains particle
the weights that particle is corresponding are:
PHD predicts: to the suggestion distribution of survival target
with the suggestion distribution p of newborn target
k(| Z
k) sample.If be carved with L during k-1
k-1individual particle, the k moment sampling population of newborn target is J
k.To i=1 ..., L
k-1, sampling
the forecast power of survival particle is:
In formula (2),
Wherein p
s,k() is target survival probability function, f
k|k-1(|) is dbjective state transfer function, b
k(|) is derivative strength function.To i=L
k-1+ 1 ..., L
k-1+ J
k, sampling
the forecast power of newborn particle is:
In formula (3), γ
k() is the strength function of newborn target.
PHD upgrades: to i=1 ..., L
k-1+ J
k, upgrading particle weights is:
In formula (4), p
d,k() is survival probability function, g
k(|) is for measuring function, K
k(z)=λ
kc
k(z), wherein λ
kfor average interference, clutter number, c
kz () is interference, noise intensity function,
Number of targets is estimated: the number of targets of moment k is estimated as:
In formula (5), int () represents round.For distinguishing different SMC-PHD wave filters, mark wave filter j estimates in the number of targets in moment:
Resampling: get
to particle collection
resampling, and weights are normalized, obtain new particle collection
the concrete grammar of resampling can see prior art.
Dbjective state is extracted: according to estimating target number, the particle after resampling is carried out cluster, cluster centre is Target state estimator.The geometry of each cluster centre is Target state estimator collection
for distinguishing different SMC-PHD wave filters, mark wave filter j estimates in the number of targets in moment:
Results analyses module, for carrying out overall treatment to obtain the state of motion of target to the Output rusults of multiple SMC-PHD wave filter.The estimated value of the number of targets of described results analyses module is:
For Target state estimator collection
comprehensive treatment device retains the result of each SMC-PHD wave filter and is shown in dbjective state spatially simultaneously, that is:
Move as example below by way of the multiple goal in a two dimensional surface, multiple mobile object Situation Awareness method of the present invention and principle of device are described in detail, such as: the state set of each target is:
wherein x
k, y
kbe respectively the location variable of target in X, Y-direction,
be respectively the speed variables of target in X, Y-direction.The generation of these targets meets Poisson point process, and the newborn strength function of these targets known is:
wherein m
1=[-150,0,25,0], m
2=[-25,0,100,0], m
2=[-25,0,100,0], m
3=[25,0,75,0], m
4=[25,0,75,0], Q=diag ([100,10,100,10]
t)
2, wherein diag (A) represents that diagonal line is the diagonal matrix of A, r
k, 1=r
k, 2=0.02, r
k, 3=r
k, 4=0.03.
The state transfer case of single target can be portrayed by following formula:
In formula, Δ is observation interval, and this example establishes Δ=1.
If the target of observing, then measurement is:
Wherein v
kfor observation noise, meet v
k~ N (, 0, R
k), and meet
wherein σ
x=σ
y=8.
Observe the movement locus of 10 targets in interval as shown in Figure 3 for 100.The survival probability of target setting in simulation process
be 0.95, detection probability
the average interference of each observation, clutter number λ
k=20, and clutter is evenly distributed in the plane of vision of [-4000,4000] × [-4000,4000].
Fig. 4 gives non-filtered, and directly according to measuring the targetpath estimated, Fig. 5 gives the targetpath that the Situation Awareness System that proposed by the present invention provides and estimates.As a comparison, Fig. 6 gives the targetpath estimated result of single SMC-PHD wave filter.In addition, Fig. 7 gives the real goal number in each moment, based on the number of targets estimated value of the Situation Awareness System that the present invention proposes, with the number of targets estimated value based on unfiltered measurement.Comparison diagram 3 ~ Fig. 6, the multi-target traces of the visible multiple mobile object Situation Awareness method based on the present invention's proposition, Apparatus and system estimates the actual flight path the most clearly reflecting target, and due to the randomness of particle filter, the targetpath result robustness of single SMC-PHD wave filter is poor, cannot clearly reflect multiobject actual flight path especially at the estimated result of targetpath access areas.According to the number of targets estimated result of Fig. 7, the number of targets based on the Situation Awareness System of the present invention's proposition is estimated to reflect actual conditions more exactly, and very large based on the number of targets estimated result error of unfiltered measurement.Therefore, the multiple mobile object Situation Awareness method that the present invention proposes, Apparatus and system can reflect multiobject true flight path more accurately, clearly, estimate the number of targets in each moment more exactly.
As shown in Figure 8, be a kind of multiple mobile object Situation Awareness System structural representation that the embodiment of the present invention provides; This system comprises: as above arbitrary described multiple mobile object Situation Awareness device.
Technical scheme of the present invention carries out analyzing and processing by adopting parallel sequential Monte Carlo and probability hypothesis density wave filter to multiple mobile object, thus make multiple mobile object Situation Awareness reflect multiobject true flight path more accurately, clearly, estimate the number of targets in each moment more exactly.
The foregoing is only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.All any amendments done within the spirit and principles in the present invention, equivalent replacement, improvement etc., be all included in protection scope of the present invention.
Claims (10)
1. a multiple mobile object Situation Awareness method, is characterized in that, comprising:
Obtain the measurement set of target;
The measurement set of described target is sent at least two parallel sequential Monte Carlo-probability hypothesis density wave filters to process, and output processing result;
Described result is comprehensively analyzed, obtains the state of motion of measurement set each target interior of described target.
2. multiple mobile object Situation Awareness method according to claim 1, is characterized in that, measures the type that type of variables depends on Situation Awareness System sensor in the measurement set of described target.
3. multiple mobile object Situation Awareness method according to claim 2, is characterized in that, described Situation Awareness System sensor comprises: active radar system and passive reconnaissance system; The measurement variable of described active radar system comprises: distance, azimuth pitch angle; The measurement variable of described passive reconnaissance system comprises: azimuth pitch angle, angular velocity, the time difference, frequency difference.
4. multiple mobile object Situation Awareness method according to claim 3, is characterized in that, described sequential Monte Carlo-probability hypothesis density wave filter, specifically comprise: initialization, probability hypothesis density is predicted, probability hypothesis density upgrades, number of targets is estimated, resampling, and dbjective state is extracted.
5. multiple mobile object Situation Awareness method as claimed in any of claims 1 to 4, it is characterized in that, the method also comprises:
Store the result of sequential Monte Carlo described in each-probability hypothesis density wave filter;
Show the state of motion of measurement set each target interior of described target.
6. a multiple mobile object Situation Awareness device, is characterized in that, comprising:
Target measures acquisition module, for obtaining the measurement set of target;
Sequential Monte Carlo-probability hypothesis density filter parallel processing module, processes for the measurement set of described target being sent at least two parallel sequential Monte Carlo-probability hypothesis density wave filters, and output processing result;
Results analyses module, for described result comprehensively being analyzed, obtains the state of motion of measurement set each target interior of described target.
7. multiple mobile object Situation Awareness device according to claim 6, is characterized in that, measures the type that type of variables depends on Situation Awareness System sensor in the measurement set of described target; Described Situation Awareness System sensor comprises: active radar system and passive reconnaissance system; The measurement variable of described active radar system comprises: distance, azimuth pitch angle; The measurement variable of described passive reconnaissance system comprises: azimuth pitch angle, angular velocity, the time difference, frequency difference.
8. multiple mobile object Situation Awareness device according to claim 7, is characterized in that, described sequential Monte Carlo-probability hypothesis density wave filter, specifically comprise: initialization, probability hypothesis density is predicted, probability hypothesis density upgrades, number of targets is estimated, resampling, and dbjective state is extracted.
9., according to the multiple mobile object Situation Awareness device in claim 6 to 8 described in any one, it is characterized in that, this device also comprises:
Memory module, for storing the result of sequential Monte Carlo described in each-probability hypothesis density wave filter;
Display module, for show described target measurement set in the state of motion of each target.
10. a multiple mobile object Situation Awareness System, is characterized in that, comprising: as the multiple mobile object Situation Awareness device in claim 6 to 9 as described in any one.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108981707A (en) * | 2018-07-25 | 2018-12-11 | 西安电子科技大学 | The passive tracking multi-target method of case particle PHD is measured based on the time difference |
| CN109031188A (en) * | 2018-06-14 | 2018-12-18 | 中国人民解放军战略支援部队信息工程大学 | A kind of narrow-band radiated source frequency difference estimation method and device based on Monte Carlo |
| CN110035024A (en) * | 2019-03-31 | 2019-07-19 | 华南理工大学 | A kind of soft demodulating method and device based on certainty sequential Monte Carlo algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5317321A (en) * | 1993-06-25 | 1994-05-31 | The United States Of America As Represented By The Secretary Of The Army | Situation awareness display device |
| CN101975575A (en) * | 2010-10-15 | 2011-02-16 | 西安电子科技大学 | Multi-target tracking method for passive sensor based on particle filtering |
| CN102288948A (en) * | 2011-05-13 | 2011-12-21 | 中国民航大学 | High-speed platform high-speed air moving target detection method based on STAP (Spacetime Adaptive Processing) |
| CN104252178A (en) * | 2014-09-12 | 2014-12-31 | 西安电子科技大学 | Strong maneuver-based target tracking method |
-
2015
- 2015-07-15 CN CN201510416468.3A patent/CN104977581A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5317321A (en) * | 1993-06-25 | 1994-05-31 | The United States Of America As Represented By The Secretary Of The Army | Situation awareness display device |
| CN101975575A (en) * | 2010-10-15 | 2011-02-16 | 西安电子科技大学 | Multi-target tracking method for passive sensor based on particle filtering |
| CN102288948A (en) * | 2011-05-13 | 2011-12-21 | 中国民航大学 | High-speed platform high-speed air moving target detection method based on STAP (Spacetime Adaptive Processing) |
| CN104252178A (en) * | 2014-09-12 | 2014-12-31 | 西安电子科技大学 | Strong maneuver-based target tracking method |
Non-Patent Citations (1)
| Title |
|---|
| 占荣辉等: "基于序贯蒙特卡罗概率假设密度滤波的多目标检测前跟踪改进算法", 《电子与信息学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109031188A (en) * | 2018-06-14 | 2018-12-18 | 中国人民解放军战略支援部队信息工程大学 | A kind of narrow-band radiated source frequency difference estimation method and device based on Monte Carlo |
| CN109031188B (en) * | 2018-06-14 | 2021-01-29 | 中国人民解放军战略支援部队信息工程大学 | Monte Carlo-based narrow-band radiation source frequency difference estimation method and device |
| CN108981707A (en) * | 2018-07-25 | 2018-12-11 | 西安电子科技大学 | The passive tracking multi-target method of case particle PHD is measured based on the time difference |
| CN108981707B (en) * | 2018-07-25 | 2020-09-22 | 西安电子科技大学 | Passive tracking multi-target method based on time difference measurement box particle PHD |
| CN110035024A (en) * | 2019-03-31 | 2019-07-19 | 华南理工大学 | A kind of soft demodulating method and device based on certainty sequential Monte Carlo algorithm |
| CN110035024B (en) * | 2019-03-31 | 2021-10-26 | 华南理工大学 | Soft demodulation method and device based on deterministic sequential Monte Carlo algorithm |
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Application publication date: 20151014 |